Spatio-temporal distribution of beaked whales in southern California waters

2014 ◽  
Vol 136 (4) ◽  
pp. 2073-2073
Author(s):  
Simone Baumann-Pickering ◽  
Jennifer S. Trickey ◽  
Marie A. Roch ◽  
Sean M. Wiggins
Keyword(s):  
Southern California ◽  
Temporal Distribution ◽  
Beaked Whales ◽  
Spatio Temporal

Spatio-temporal distribution of beaked whales on Canada’s East Coast

2017 ◽  
Vol 141 (5) ◽  
pp. 3939-3939
Author(s):  
Bruce Martin ◽  
Julien Delarue ◽  
Katie Kowarski ◽  
Hilary Moors-Murphy ◽  
Joanna Mills Flemming
Keyword(s):  
East Coast ◽  
Temporal Distribution ◽  
Beaked Whales ◽  
Spatio Temporal

Analysis of spatio-temporal distribution of mortality due to gastric and colon cancer in Hungary and in Japan between 1986–2002

2008 ◽  
Vol 46 (05) ◽  
Author(s):  
K Tamássy ◽  
A Páldy ◽  
G Nádor ◽  
A Laborczi ◽  
L Pásztor ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Temporal Distribution ◽  
Spatio Temporal

Monitoring the spatio-temporal distribution of fish in Pohang-Namgu marine ranching area using acoustic survey

2019 ◽  
Vol 12 (1) ◽  
pp. 8-13
Author(s):  
Min-Seok KIM ◽  
Jun-Woo HWANG ◽  
Eun-Bi MIN ◽  
Eun-A YOON ◽  
Doo-Jin HWANG
Keyword(s):  
Temporal Distribution ◽  
Acoustic Survey ◽  
Spatio Temporal ◽  
Distribution Of Fish

Influence of Planting Density on Root Spatio-Temporal Distribution of Different Types of Maize under High-Yielding Cultivation Conditions

2013 ◽  
Vol 38 (7) ◽  
pp. 1286-1294 ◽  
Author(s):  
Zong-Xin LI ◽  
Yuan-Quan CHEN ◽  
Qing-Cheng WANG ◽  
Kai-Chang LIU ◽  
Wang-Sheng GAO ◽  
...  
Keyword(s):  
Temporal Distribution ◽  
Planting Density ◽  
Cultivation Conditions ◽  
Different Types ◽  
Spatio Temporal

Modeling of Habitat and Foraging Behavior of Beaked Whales in the Southern California Bight

2014 ◽  
Author(s):  
Simone Baumann-Pickering ◽  
John A. Hildebrand ◽  
Tina Yack ◽  
Jeffrey E. Moore
Keyword(s):  
Foraging Behavior ◽  
Southern California ◽  
Southern California Bight ◽  
Beaked Whales

SPATIO-TEMPORAL DISTRIBUTION OF URANIUM CONCENTRATION IN SHALLOW AQUIFERS OF ARAVALLI TERRANE (NORTH GUJARAT), INDIA

2019 ◽  
Author(s):  
Rudra Mohan Pradhan ◽  
◽  
Karrie A. Weber ◽  
Karrie A. Weber ◽  
Daniel Snow ◽  
...  
Keyword(s):  
Uranium Concentration ◽  
Temporal Distribution ◽  
Shallow Aquifers ◽  
Spatio Temporal ◽  
North Gujarat

scholarly journals Variation Characteristics and Transportation of Aerosol, NO2, SO2, and HCHO in Coastal Cities of Eastern China: Dalian, Qingdao, and Shanghai

2021 ◽  
Vol 13 (5) ◽  
pp. 892
Author(s):  
Xiaomei Li ◽  
Pinhua Xie ◽  
Ang Li ◽  
Jin Xu ◽  
Zhaokun Hu ◽  
...  
Keyword(s):  
Eastern China ◽  
Temporal Distribution ◽  
Correlation Coefficients ◽  
Optical Absorption Spectroscopy ◽  
Near Surface ◽  
Mixing Ratios ◽  
Variation Characteristics ◽  
Spatio Temporal ◽  
Gas Volume ◽  
Transport Flux

This paper studied the method for converting the aerosol extinction to the mass concentration of particulate matter (PM) and obtained the spatio-temporal distribution and transportation of aerosol, nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO) based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations in Dalian (38.85°N, 121.36°E), Qingdao (36.35°N, 120.69°E), and Shanghai (31.60°N, 121.80°E) from 2019 to 2020. The PM2.5 measured by the in situ instrument and the PM2.5 simulated by the conversion formula showed a good correlation. The correlation coefficients R were 0.93 (Dalian), 0.90 (Qingdao), and 0.88 (Shanghai). A regular seasonality of the three trace gases is found, but not for aerosols. Considerable amplitudes in the weekly cycles were determined for NO2 and aerosols, but not for SO2 and HCHO. The aerosol profiles were nearly Gaussian, and the shapes of the trace gas profiles were nearly exponential, except for SO2 in Shanghai and HCHO in Qingdao. PM2.5 presented the largest transport flux, followed by NO2 and SO2. The main transport flux was the output flux from inland to sea in spring and winter. The MAX-DOAS and the Copernicus Atmosphere Monitoring Service (CAMS) models’ results were compared. The overestimation of NO2 and SO2 by CAMS is due to its overestimation of near-surface gas volume mixing ratios.

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